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Minimize RSR Award Detail

Research Spending & Results

Award Detail

Awardee:UNIVERSITY SYSTEM OF NEW HAMPSHIRE
Doing Business As Name:University of New Hampshire
PD/PI:
  • Wheeler Ruml
  • (603) 862-2683
  • ruml@cs.unh.edu
Award Date:08/13/2020
Estimated Total Award Amount: $ 499,807
Funds Obligated to Date: $ 499,807
  • FY 2020=$499,807
Start Date:10/01/2020
End Date:09/30/2023
Transaction Type:Grant
Agency:NSF
Awarding Agency Code:4900
Funding Agency Code:4900
CFDA Number:47.070
Primary Program Source:040100 NSF RESEARCH & RELATED ACTIVIT
Award Title or Description:NSF-BSF: RI: Small: Planning and Acting While Time Passes
Federal Award ID Number:2008594
DUNS ID:111089470
Parent DUNS ID:001765866
Program:Robust Intelligence
Program Officer:
  • Roger Mailler
  • (703) 292-7982
  • rmailler@nsf.gov

Awardee Location

Street:51 COLLEGE RD SERVICE BLDG 107
City:Durham
State:NH
ZIP:03824-3585
County:Durham
Country:US
Awardee Cong. District:01

Primary Place of Performance

Organization Name:University of New Hampshire
Street:105 Main St
City:Durham
State:NH
ZIP:03824-2619
County:Durham
Country:US
Cong. District:01

Abstract at Time of Award

Planning allows intelligent systems to select actions aimed towards achieving their goals. However, traditional planning methods assume that the world evolves slowly enough, or that the problems to be solved are sufficiently simple, that the world can be considered static during planning. This limitation means that most current planners are unable, for example, to realize that it might be better to quickly find a suboptimal plan to take the bus that is about to leave, rather than to carefully deliberate about optimal plans and thereby miss the bus altogether. Currently, planning representations and algorithms are laboriously manually engineered to ensure that the system responds quickly enough for the intended application, essentially ducking the issue of the passage of time while the system is planning. This project enables more robust and general-purpose intelligent systems by developing new "situated planning" methods that reason about their own reasoning enough to overcome this limitation. The project will consider two settings for situated planning. The first is the traditional batch setting, in which all decisions are made before plan execution begins. Three challenges will be addressed: 1) Formalizing a model of planning while time passes and analyzing its computational complexity, 2) Simplifying the resulting "reasoning about reasoning" problem enough that it can be approximately solved repeatedly during the planning process, including identifying tractable subclasses and greedy heuristics, and 3) Estimating the information needed for doing this reasoning on-line. The second setting is incremental planning, where execution of actions can be interleaved with additional planning. Three additional challenges will be addressed: 4) Formalizing situated planning with action costs , 5) Developing a continual situated planner that improves a plan while it is being executed, and 6) Addressing online situated planning, where actions can be dispatched for execution before a complete plan has been found. Solving these situated planning problems will result in practical and flexible planners that can smoothly interpolate their behavior in a time-aware way between batch and incremental as appropriate, thereby broadening the range of applications that can be addressed by intelligent systems. Project results will be integrated into the open source OPTIC planner and ROSPlan robot control framework. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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